A Simple and Efficient Federated Recommender System.ipynb

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      "name": "Experiment_FL_2.ipynb",
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        "!pip install syft"
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            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.22.0 which is incompatible.\u001b[0m\n",
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      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement PIL (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for PIL\u001b[0m\n"
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          "name": "stdout"
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      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_ZrXJBRgvaQG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "b35b7c21-1079-4bc7-a8b7-35daae4d0d17"
      },
      "source": [
        "!git clone https://github.com/tusharsoni08/Openmined-FL-Recommendation-System.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Openmined-FL-Recommendation-System'...\n",
            "remote: Enumerating objects: 21, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
            "remote: Total 21 (delta 6), reused 15 (delta 3), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (21/21), 630.86 KiB | 716.00 KiB/s, done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MDUHl-wDwElx",
        "colab": {},
        "outputId": "0f25b17e-7734-4be3-a734-f1d627bd874f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import syft as sy\n",
        "import time\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "hook = sy.TorchHook(torch)\n",
        "class Arguments():\n",
        "    def __init__(self):\n",
        "        self.batch_size = 1\n",
        "        self.test_batch_size = 50\n",
        "        self.seed = 1\n",
        "#bob = sy.VirtualWorker(hook, id=\"bob\")  # <-- NEW: define remote worker bob\n",
        "#alice = sy.VirtualWorker(hook, id=\"alice\")  # <-- NEW: and alice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Torch was already hooked... skipping hooking process\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vUVo8n5WsA6I",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import random\n",
        "import pandas as pd\n",
        "from copy import deepcopy\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        " \n",
        "random.seed(0)\n",
        " \n",
        " \n",
        "class UserItemRatingDataset(Dataset):\n",
        "    \"\"\"Wrapper, convert <user, item, rating> Tensor into Pytorch Dataset\"\"\"\n",
        "    def __init__(self, user_tensor, item_tensor, target_tensor):\n",
        "        \"\"\"\n",
        "        args:\n",
        "            target_tensor: torch.Tensor, the corresponding rating for <user, item> pair\n",
        "        \"\"\"\n",
        "        self.user_tensor = user_tensor\n",
        "        self.item_tensor = item_tensor\n",
        "        self.target_tensor = target_tensor\n",
        " \n",
        "    def __getitem__(self, index):\n",
        "        data_tensor=torch.stack([self.user_tensor[index], self.item_tensor[index]],dim=0)\n",
        "        # print(data_tensor.shape)\n",
        "        return data_tensor, self.target_tensor[index]\n",
        " \n",
        "    def __len__(self):\n",
        "        return len(self.user_tensor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oJrjiE9n4USa",
        "colab": {}
      },
      "source": [
        "# sample_generator = SampleGenerator(ratings=ml1m_rating)\n",
        "# # base_federated=dataset.federate((bob, alice))\n",
        "# base = sample_generator.instance_a_train_loader(4, 32)\n",
        "rs_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
        "train_data = pd.read_csv('Openmined-FL-Recommendation-System/Data/ua.base', sep='\\t', names=rs_cols, encoding='latin-1')\n",
        "user_ids = train_data[\"user_id\"].unique().tolist()\n",
        "user2user_encoded = {x: i for i, x in enumerate(user_ids)}\n",
        "userencoded2user = {i: x for i, x in enumerate(user_ids)}\n",
        "movie_ids = train_data[\"movie_id\"].unique().tolist()\n",
        "movie2movie_encoded = {x: i for i, x in enumerate(movie_ids)}\n",
        "movie_encoded2movie = {i: x for i, x in enumerate(movie_ids)}\n",
        "train_data[\"user\"] = train_data[\"user_id\"].map(user2user_encoded)\n",
        "train_data[\"movie\"] = train_data[\"movie_id\"].map(movie2movie_encoded)\n",
        "\n",
        "num_users = len(user2user_encoded)\n",
        "num_movies = len(movie_encoded2movie)\n",
        "train_data[\"rating\"] = train_data[\"rating\"].values.astype(np.float32)\n",
        "# federated_SVHN=UserItemRatingDataset(torch.LongTensor(train_data[\"user\"]),torch.LongTensor(train_data[\"movie\"]),torch.FloatTensor(train_data[\"rating\"]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hNLOaElF3SZs",
        "colab": {}
      },
      "source": [
        "#workers=[bob,alice]\n",
        "#federated_train_loader = sy.FederatedDataLoader( # <-- this is now a FederatedDataLoader \n",
        "#                         UserItemRatingDataset(torch.LongTensor(train_data[\"user\"]),torch.LongTensor(train_data[\"movie\"]),torch.FloatTensor(train_data[\"rating\"])).federate(tuple(workers)),batch_size=1024,shuffle=True,iter_per_worker=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aPhk5DiXE1Cb",
        "colab": {}
      },
      "source": [
        "#Create virtual workers\n",
        "\n",
        "workers = []\n",
        "\n",
        "for user_id in user_ids:\n",
        "    worker = sy.VirtualWorker(hook, id=\"user_\"+str(user_id))\n",
        "    workers.append(worker)\n",
        "\n",
        "federated_train_loader = sy.FederatedDataLoader( # <-- this is now a FederatedDataLoader \n",
        "                         UserItemRatingDataset(torch.LongTensor(train_data[\"user\"]),torch.LongTensor(train_data[\"movie\"]),torch.FloatTensor(train_data[\"rating\"])).federate(tuple(workers)),batch_size=1024,shuffle=True,iter_per_worker=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QUH2RBnISmIo",
        "colab": {}
      },
      "source": [
        "class Model(nn.Module):\n",
        "    def __init__(self, num_users, num_movies, embedding_size):\n",
        "        super(Model, self).__init__()\n",
        "        self.num_users = num_users\n",
        "        self.num_movies = num_movies\n",
        "        self.embedding_size = embedding_size\n",
        "        \n",
        "        self.user_embedding = nn.Embedding(num_users, embedding_size)\n",
        "        self.movie_embedding = nn.Embedding(num_movies, embedding_size)\n",
        "        \n",
        "        self.fc_layers = nn.ModuleList()\n",
        "        self.fc_layers.append(nn.Linear(50, 50))\n",
        "        self.output_layer = nn.Linear(embedding_size, 1)\n",
        "\n",
        "    def forward(self, train_data):\n",
        "        users = torch.as_tensor(train_data[:,0])\n",
        "        movies = torch.as_tensor(train_data[:,1])\n",
        "        user_embedding_x = self.user_embedding(users)\n",
        "        movie_embedding_y = self.movie_embedding(movies)\n",
        "        prod=torch.mul(user_embedding_x,movie_embedding_y)\n",
        "        # concatenate user and movie embeddings to form input to 1 dim\n",
        "        # x = torch.cat([user_embedding_x, movie_embedding_y], 1)\n",
        "        \n",
        "        # for idx, _ in enumerate(range(len(self.fc_layers))):\n",
        "        #     x = self.fc_layers[idx](x)\n",
        "        #     x = F.dropout(x, p=0.2)\n",
        "        #     x = F.batch_norm(x)\n",
        "        #     x = F.relu(x)\n",
        "        \n",
        "        logit = self.output_layer(prod)\n",
        "        # The sigmoid activation forces the rating to between 0 and 1\n",
        "        rating = torch.sigmoid(logit)\n",
        "        return rating\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vn8ANheJlGuH",
        "colab": {}
      },
      "source": [
        "# from syft.frameworks.torch.fl import utils\n",
        "# embed_size=50\n",
        "# model = Model(num_users,num_movies,embed_size)\n",
        "# optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
        "# lr=0.1\n",
        "# def trainn():\n",
        "#     for epoch in range(0, 5):\n",
        "#         for batch_idx, (data, target) in enumerate(federated_train_loader):\n",
        "#             print(data.location)\n",
        "#             print(target.location)\n",
        "#             # send the model to the client device where the data is present\n",
        "#             model.send(data.location)\n",
        "#             # training the model\n",
        "#             optimizer.zero_grad()\n",
        "#             prediction = model(data)\n",
        "#             loss = F.mse_loss(prediction.view(-1), target)\n",
        "#             loss.backward()\n",
        "#             optimizer.step()\n",
        "#             # get back the improved model\n",
        "#             print(loss.get())\n",
        "#             model.get()\n",
        "#             return utils.federated_avg({\n",
        "#                 \"model\": model\n",
        "#             })"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pHLwvjSc6N31",
        "colab": {}
      },
      "source": [
        "\n",
        "# for epoch in range(5):\n",
        "#     start_time = time.time()\n",
        "#     print(\"Epoch Number {epoch + 1}\")\n",
        "#     federated_model = trainn()\n",
        "#     model = federated_model\n",
        "# #     test(federated_model)\n",
        "#     total_time = time.time() - start_time\n",
        "#     print('Communication time over the network', round(total_time, 2), 's\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GalDOGKExaHt",
        "colab": {}
      },
      "source": [
        "from syft.frameworks.torch.fl import utils\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as f\n",
        "def train_on_batches(worker, batches, model_in, device, lr):\n",
        "    \"\"\"Train the model on the worker on the provided batches\n",
        "    Args:\n",
        "        worker(syft.workers.BaseWorker): worker on which the\n",
        "        training will be executed\n",
        "        batches: batches of data of this worker\n",
        "        model_in: machine learning model, training will be done on a copy\n",
        "        device (torch.device): where to run the training\n",
        "        lr: learning rate of the training steps\n",
        "    Returns:\n",
        "        model, loss: obtained model and loss after training\n",
        "    \"\"\"\n",
        "    model = model_in.copy()\n",
        "    optimizer = optim.SGD(model.parameters(), lr=lr)  # TODO momentum is not supported at the moment\n",
        "\n",
        "    model.train()\n",
        "    model.send(worker)\n",
        "    loss_local = False\n",
        "    LOG_INTERVAL=25\n",
        "\n",
        "    for batch_idx, (data, target) in enumerate(batches):\n",
        "        loss_local = False\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = f.mse_loss(output.view(-1), target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % LOG_INTERVAL == 0:\n",
        "            loss = loss.get()  # <-- NEW: get the loss back\n",
        "            loss_local = True\n",
        "            print(\n",
        "                \"Train Worker {}: [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\".format(\n",
        "                    worker.id,\n",
        "                    batch_idx,\n",
        "                    len(batches),\n",
        "                    100.0 * batch_idx / len(batches),\n",
        "                    loss.item(),\n",
        "                )\n",
        "            )\n",
        "\n",
        "    if not loss_local:\n",
        "      loss = loss.get()  # <-- NEW: get the loss back\n",
        "    model.get()  # <-- NEW: get the model back\n",
        "    return model, loss\n",
        "\n",
        "\n",
        "def get_next_batches(fdataloader: sy.FederatedDataLoader, nr_batches: int):\n",
        "    \"\"\"retrieve next nr_batches of the federated data loader and group\n",
        "    the batches by worker\n",
        "    Args:\n",
        "        fdataloader (sy.FederatedDataLoader): federated data loader\n",
        "        over which the function will iterate\n",
        "        nr_batches (int): number of batches (per worker) to retrieve\n",
        "    Returns:\n",
        "        Dict[syft.workers.BaseWorker, List[batches]]\n",
        "    \"\"\"\n",
        "    batches = {}\n",
        "    for worker_id in fdataloader.workers:\n",
        "        worker = fdataloader.federated_dataset.datasets[worker_id].location\n",
        "        batches[worker] = []\n",
        "    try:\n",
        "        for i in range(nr_batches):\n",
        "            next_batches = next(fdataloader)\n",
        "            for worker in next_batches:\n",
        "                batches[worker].append(next_batches[worker])\n",
        "    except StopIteration:\n",
        "        pass\n",
        "    return batches\n",
        "\n",
        "\n",
        "def train(\n",
        "    model, device, federated_train_loader, lr, federate_after_n_batches, abort_after_one=False\n",
        "):\n",
        "    model.train()\n",
        "\n",
        "    nr_batches = federate_after_n_batches\n",
        "\n",
        "    models = {}\n",
        "    loss_values = {}\n",
        "\n",
        "    iter(federated_train_loader)  # initialize iterators\n",
        "    batches = get_next_batches(federated_train_loader, nr_batches)\n",
        "    counter = 0\n",
        "\n",
        "    while True:\n",
        "        print(f\"Starting training round, batches [{counter}, {counter + nr_batches}]\")\n",
        "        data_for_all_workers = True\n",
        "        for worker in batches:\n",
        "            curr_batches = batches[worker]\n",
        "            if curr_batches:\n",
        "                models[worker], loss_values[worker] = train_on_batches(\n",
        "                    worker, curr_batches, model, device, lr\n",
        "                )\n",
        "            else:\n",
        "                data_for_all_workers = False\n",
        "        counter += nr_batches\n",
        "        if not data_for_all_workers:\n",
        "            print(\"At least one worker ran out of data, stopping.\")\n",
        "            break\n",
        "\n",
        "        model = utils.federated_avg(models)\n",
        "        batches = get_next_batches(federated_train_loader, nr_batches)\n",
        "        if abort_after_one:\n",
        "            break\n",
        "    return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FRhwZABHxctO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "b5c010ef-e85b-45ef-8442-24ab3c6627c8"
      },
      "source": [
        "embed_size=50\n",
        "model = Model(num_users,num_movies,embed_size)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
        "lr=0.1\n",
        "federate_after_n_batches=50\n",
        "device=torch.device(\"cpu\")\n",
        "for epoch in range(1, 5):\n",
        "        print(\"Starting epoch {}/{}\".format(epoch,5))\n",
        "        model = train(model, device, federated_train_loader, lr, federate_after_n_batches)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting epoch 1/5\n",
            "Starting training round, batches [0, 50]\n",
            "Train Worker user_1: [0/1 (0%)]\tLoss: 11.610948\n",
            "Train Worker user_2: [0/1 (0%)]\tLoss: 12.028971\n",
            "Train Worker user_3: [0/1 (0%)]\tLoss: 10.711548\n",
            "Train Worker user_4: [0/1 (0%)]\tLoss: 9.428104\n",
            "Train Worker user_5: [0/1 (0%)]\tLoss: 8.340612\n",
            "Train Worker user_6: [0/1 (0%)]\tLoss: 8.466829\n",
            "Train Worker user_7: [0/1 (0%)]\tLoss: 9.085833\n",
            "Train Worker user_8: [0/1 (0%)]\tLoss: 13.865302\n",
            "Train Worker user_9: [0/1 (0%)]\tLoss: 14.810122\n",
            "Train Worker user_10: [0/1 (0%)]\tLoss: 13.069444\n",
            "Train Worker user_11: [0/1 (0%)]\tLoss: 11.581843\n",
            "Train Worker user_12: [0/1 (0%)]\tLoss: 12.834407\n",
            "Train Worker user_13: [0/1 (0%)]\tLoss: 14.090590\n",
            "Train Worker user_14: [0/1 (0%)]\tLoss: 13.588277\n",
            "Train Worker user_15: [0/1 (0%)]\tLoss: 10.072558\n",
            "Train Worker user_16: [0/1 (0%)]\tLoss: 10.464640\n",
            "Train Worker user_17: [0/1 (0%)]\tLoss: 11.153332\n",
            "Train Worker user_18: [0/1 (0%)]\tLoss: 10.725760\n",
            "Train Worker user_19: [0/1 (0%)]\tLoss: 6.666382\n",
            "Train Worker user_20: [0/1 (0%)]\tLoss: 10.236942\n",
            "Train Worker user_21: [0/1 (0%)]\tLoss: 9.486159\n",
            "Train Worker user_22: [0/1 (0%)]\tLoss: 7.359022\n",
            "Train Worker user_23: [0/1 (0%)]\tLoss: 8.704612\n",
            "Train Worker user_24: [0/1 (0%)]\tLoss: 11.768733\n",
            "Train Worker user_25: [0/1 (0%)]\tLoss: 11.270159\n",
            "Train Worker user_26: [0/1 (0%)]\tLoss: 14.995612\n",
            "Train Worker user_27: [0/1 (0%)]\tLoss: 12.210484\n",
            "Train Worker user_28: [0/1 (0%)]\tLoss: 12.546252\n",
            "Train Worker user_29: [0/1 (0%)]\tLoss: 10.879966\n",
            "Train Worker user_30: [0/1 (0%)]\tLoss: 9.280710\n",
            "Train Worker user_31: [0/1 (0%)]\tLoss: 4.639063\n",
            "Train Worker user_32: [0/1 (0%)]\tLoss: 11.380510\n",
            "Train Worker user_33: [0/1 (0%)]\tLoss: 9.025133\n",
            "Train Worker user_34: [0/1 (0%)]\tLoss: 11.840882\n",
            "Train Worker user_35: [0/1 (0%)]\tLoss: 13.784966\n",
            "Train Worker user_36: [0/1 (0%)]\tLoss: 7.143606\n",
            "Train Worker user_37: [0/1 (0%)]\tLoss: 10.498229\n",
            "Train Worker user_38: [0/1 (0%)]\tLoss: 11.203531\n",
            "Train Worker user_39: [0/1 (0%)]\tLoss: 10.746174\n",
            "Train Worker user_40: [0/1 (0%)]\tLoss: 12.302655\n",
            "Train Worker user_41: [0/1 (0%)]\tLoss: 10.678328\n",
            "Train Worker user_42: [0/1 (0%)]\tLoss: 13.199511\n",
            "Train Worker user_43: [0/1 (0%)]\tLoss: 10.553055\n",
            "Train Worker user_44: [0/1 (0%)]\tLoss: 12.285043\n",
            "Train Worker user_45: [0/1 (0%)]\tLoss: 10.498839\n",
            "Train Worker user_46: [0/1 (0%)]\tLoss: 11.160568\n",
            "Train Worker user_47: [0/1 (0%)]\tLoss: 10.225864\n",
            "Train Worker user_48: [0/1 (0%)]\tLoss: 10.191626\n",
            "Train Worker user_49: [0/1 (0%)]\tLoss: 7.012165\n",
            "Train Worker user_50: [0/1 (0%)]\tLoss: 6.418622\n",
            "Train Worker user_51: [0/1 (0%)]\tLoss: 14.064058\n",
            "Train Worker user_52: [0/1 (0%)]\tLoss: 11.142209\n",
            "Train Worker user_53: [0/1 (0%)]\tLoss: 11.600274\n",
            "Train Worker user_54: [0/1 (0%)]\tLoss: 10.297380\n",
            "Train Worker user_55: [0/1 (0%)]\tLoss: 11.728958\n",
            "Train Worker user_56: [0/1 (0%)]\tLoss: 12.060896\n",
            "Train Worker user_57: [0/1 (0%)]\tLoss: 14.790827\n",
            "Train Worker user_58: [0/1 (0%)]\tLoss: 12.393909\n",
            "Train Worker user_59: [0/1 (0%)]\tLoss: 12.690381\n",
            "Train Worker user_60: [0/1 (0%)]\tLoss: 11.481750\n",
            "Train Worker user_61: [0/1 (0%)]\tLoss: 13.173187\n",
            "Train Worker user_62: [0/1 (0%)]\tLoss: 11.819713\n",
            "Train Worker user_63: [0/1 (0%)]\tLoss: 10.510879\n",
            "Train Worker user_64: [0/1 (0%)]\tLoss: 7.839513\n",
            "Train Worker user_65: [0/1 (0%)]\tLoss: 7.644332\n",
            "Train Worker user_66: [0/1 (0%)]\tLoss: 11.355605\n",
            "Train Worker user_67: [0/1 (0%)]\tLoss: 9.871877\n",
            "Train Worker user_68: [0/1 (0%)]\tLoss: 11.846087\n",
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            "Starting training round, batches [50, 100]\n",
            "At least one worker ran out of data, stopping.\n",
            "Starting epoch 2/5\n",
            "Starting training round, batches [0, 50]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-394e44eb8a86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Starting epoch {}/{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfederated_train_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfederate_after_n_batches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-14-713b67a40a6f>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, device, federated_train_loader, lr, federate_after_n_batches, abort_after_one)\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcurr_batches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m                 models[worker], loss_values[worker] = train_on_batches(\n\u001b[0;32m---> 96\u001b[0;31m                     \u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m                 )\n\u001b[1;32m     98\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-14-713b67a40a6f>\u001b[0m in \u001b[0;36mtrain_on_batches\u001b[0;34m(worker, batches, model_in, device, lr)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mloss_local\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mLOG_INTERVAL\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/frameworks/torch/hook/hook.py\u001b[0m in \u001b[0;36mmodule_send_\u001b[0;34m(nn_self, force_send, *dest, **kwargs)\u001b[0m\n\u001b[1;32m    611\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0melement_iter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtensor_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_self\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0melement_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 613\u001b[0;31m                     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPlan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/frameworks/torch/tensors/interpreters/native.py\u001b[0m in \u001b[0;36msend_\u001b[0;34m(self, *location, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Inplace send to several workers is currently not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m     def create_pointer(\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/frameworks/torch/tensors/interpreters/native.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, inplace, user, local_autograd, requires_grad, preinitialize_grad, no_wrap, garbage_collect_data, *location)\u001b[0m\n\u001b[1;32m    473\u001b[0m                 \u001b[0mrequires_grad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequires_grad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m                 \u001b[0mpreinitialize_grad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreinitialize_grad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m                 \u001b[0mgarbage_collect_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgarbage_collect_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m             )\n\u001b[1;32m    477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, obj, workers, ptr_id, garbage_collect_data, requires_grad, create_pointer, **kwargs)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;31m# Send the object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_obj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrequires_grad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_obj\u001b[0;34m(self, obj, location)\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0mreceive\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m         \"\"\"\n\u001b[0;32m--> 735\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mObjectMessage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    736\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m     def request_obj(\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_msg\u001b[0;34m(self, message, location)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0;31m# Step 1: serialize the message to a binary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mbin_message\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;31m# Step 2: send the message and wait for a response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/serde.py\u001b[0m in \u001b[0;36mserialize\u001b[0;34m(obj, worker, simplified, force_full_simplification, strategy)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mbinary\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mserialized\u001b[0m \u001b[0mform\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \"\"\"\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mstrategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplified\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_full_simplification\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36mserialize\u001b[0;34m(obj, worker, simplified, force_full_simplification)\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0mworker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msyft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m     \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_serialize_msgpack_simple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplified\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_full_simplification\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_serialize_msgpack_binary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimple_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_serialize_msgpack_simple\u001b[0;34m(obj, worker, simplified, force_full_simplification)\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_force_full_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m             \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0msimple_objects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_simplify\u001b[0;34m(worker, obj, **kwargs)\u001b[0m\n\u001b[1;32m    426\u001b[0m     \u001b[0;31m# print(current_type, current_type in simplifiers)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcurrent_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msimplifiers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcurrent_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minherited_simplifiers_found\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/messaging/message.py\u001b[0m in \u001b[0;36msimplify\u001b[0;34m(worker, msg)\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m         \"\"\"\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserde\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/serde.py\u001b[0m in \u001b[0;36m_simplify\u001b[0;34m(worker, obj, **kwargs)\u001b[0m\n\u001b[1;32m    426\u001b[0m     \u001b[0;31m# print(current_type, current_type in simplifiers)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcurrent_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msimplifiers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimplifiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcurrent_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minherited_simplifiers_found\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/torch_serde.py\u001b[0m in \u001b[0;36m_simplify_torch_parameter\u001b[0;34m(worker, param)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"child\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPointerTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m         \u001b[0mgrad_ser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_simplify_torch_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0mgrad_ser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/torch_serde.py\u001b[0m in \u001b[0;36m_simplify_torch_tensor\u001b[0;34m(worker, tensor)\u001b[0m\n\u001b[1;32m    122\u001b[0m     \"\"\"\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     \u001b[0mtensor_bin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_serialize_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;31m# note we need to do this explicitly because torch.save does not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/serde/msgpack/torch_serde.py\u001b[0m in \u001b[0;36m_serialize_tensor\u001b[0;34m(worker, tensor)\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;34mf\"Tensor serialization strategy is not supported: {worker.serializer}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         )\n\u001b[0;32m---> 53\u001b[0;31m     \u001b[0mserializer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mserializers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserializer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mserializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/OpenMined/venv/lib/python3.6/site-packages/syft/workers/base.py\u001b[0m in \u001b[0;36mserializer\u001b[0;34m(self, workers)\u001b[0m\n\u001b[1;32m   1215\u001b[0m         \u001b[0mframeworks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mworker\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m                 \u001b[0mframework\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wZKVh-bFyagu",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}